Detailing the Regime Shifts in Maine Coastal Current Behavior
Published
August 19, 2025
Code
{library(sf) library(fvcom) library(tidyverse)library(gmRi)library(patchwork)library(rnaturalearth)library(showtext)library(ncdf4)# Cyclic color palettes in scico# From: https://www.fabiocrameri.ch/colourmaps/library(scico)library(legendry)library(ggh4x)}# namespace conflictsconflicted::conflict_prefer("select", "dplyr")conflicted::conflict_prefer("filter", "dplyr")# Set the themetheme_set(theme_gmri_simple() +theme(strip.text.y =element_text(angle =0),legend.position ="bottom", legend.title.position ="top", legend.title =element_text(hjust =0.5), strip.text =element_text(size =8),axis.text =element_text(size =8)))# Project pathslob_ecol_path <-cs_path("mills", "Projects/Lobster ECOL")fvcom_path <-cs_path("res", "FVCOM/Lobster-ECOL")poly_paths <-cs_path("mills", "Projects/Lobster ECOL/Spatial_Defs")# Shapefilesnew_england <-ne_states("united states of america", returnclass ="sf") %>%filter(postal %in%c("VT", "ME", "RI", "MA", "CT", "NH", "NY", "MD", "VA", "NJ", "DE", "NC", "PA", "WV"))canada <-ne_states("canada", returnclass ="sf")# # # Support functions for FVCOMsource(here::here("R/FVCOM_Support.R"))# New areas factor levelsareas_northsouth <-c("eastern maine", "central maine", "western maine", "eastern mass","southern mass", "rhode island shore","long island sound", "new jersey shore", "five fathom bank", "virginia shore","gom_gbk", "sne")
Code
# Use GMRI styleuse_gmri_style_rmd()
Code
# Read Regions inproj_path <-cs_path("mills", "Projects/Lobster ECOL")# Load Shapefiles for inshore/offshorepoly_paths <-cs_path("mills", "Projects/Lobster ECOL/Spatial_Defs")# Load inshore areas# 12nm from shore, aggregates of statistical zonesinshore_areas <-map_dfr(setNames(list.files(str_c(poly_paths, "inshore_areas"), full.names = T),str_remove(list.files(str_c(poly_paths, "inshore_areas")), ".geojson")),function(x){read_sf(x)}) offshore_areas <-map_dfr(setNames(list.files(str_c(poly_paths, "offshore_areas"), full.names = T),str_remove(list.files(str_c(poly_paths, "offshore_areas")), ".geojson")),function(x){read_sf(x)}) %>%mutate(SHORT_NAME = Region) # st_crs(offshore_areas)# st_crs(inshore_areas)# Combine so we can plot themstudy_regions <-bind_rows(st_transform(inshore_areas, crs =st_crs(4269)), offshore_areas)# NEW Areas
Code
# clusters of statistical areas that align loosely with geography and management areasinshore_areas <-read_sf(str_c(poly_paths,"spatial_defs_2025/12nm_poly_statarea_merge.shp")) %>% janitor::clean_names() %>%mutate(area_type ="nearshore-coastal",area_id =tolower(short_name))# ecological production unitsoffshore_areas <-read_sf(str_c(poly_paths,"spatial_defs_2025/sne_gom_tocoast.shp")) %>% janitor::clean_names() %>%mutate(area_type ="offshore-regional",area_id =tolower(region))# Combine themstudy_regions <-bind_rows(st_transform(dplyr::select(inshore_areas, area_id, geometry), st_crs(offshore_areas)), dplyr::select(offshore_areas, area_id, geometry))
Reviewing STARS Regime Changes in Space/Time
This markdown reviews the various rstars regime shift results which were produced separately. I will begin at the largest geographic scales and work down to local timeseries:
Regime shifts for individual timeseries were tested using the STARS methodology. Any daily timeseries (temperature and salinity from ocean reanalysis models) were aggregated to a monthly temporal resolution, and any trends and seasonal cycles were removed.
The Marriott, Pope and Kendall (MPK) “pre-whitening” routine was used within the {rstars} algorithm to remove “red noise” (autoregressive processes, typically AR1) from the timeseries.
For more details on trend removal and pre-whitening methods see Rodionov 2006.
Shelf-Scale Shifts
There are 2-3 climate and oceanographic timeseries that operate at the broad regional scale of the Northeast shelf. These are:
The Gulf Stream Index (a metric indicating the North/South position of the Gulf Stream) based on SSH
The Northeast Channel Slopewater Proportions (the percentage of various water masses at the 150-200m depth entering GOM, using NERACOOS buoy data)
The North Atlantic Oscillation (atmospheric pressure differential between icelandic low and the Azores High)
These three indices affect conditions over large spatial scales and and are likely to either directly or indirectly impact more local scale changes.
These three metrics are available in the ecodata r package and can be pulled directly from the package.
Code
library(ecodata)# GSI# Why is there more than one value per month?gsi <- ecodata::gsi %>%#glimpse()mutate(Time =as.Date(str_c(str_replace(Time, "[.]", "-"), "-01")))# use the old onegsi_old <- ecodata::gsi_old %>%#glimpse()mutate(Var ="gulf stream index old") %>%mutate(Time =as.Date(str_c(str_replace(Time, "[.]", "-"), "-01")))# NAOnao <- ecodata::nao%>%mutate(Time =as.Date(str_c(Time, "-01-01")))# Put them together to plotshelf_indices <-bind_rows(list(gsi, gsi_old, nao))
The Gulf Stream indices come as two monthly datasets, the other indices are annual. Any long-term trends for each metric have been removed prior to regime shift tests on these metrics.
Code
# Run the shift test for summertime PCAshelf_indices_detrended <- shelf_indices %>%split(.$Var) %>%map_dfr(function(.x){# Detrend .x <- .x %>%arrange(Time) %>%mutate(time = Time,yr_num =year(time))# annual trend trend_mod <-lm(Value ~ Time, data = .x)# save the results .x <- broom::augment(x = trend_mod) %>%rename(trend_fit = .fitted,trend_resid = .resid) %>%full_join(.x, join_by(Time, Value)) %>%mutate(trend_resid =if_else(is.na(Value), NA, trend_resid))return(.x)})# Plot the residuals from the trendggplot(shelf_indices_detrended, aes(Time, trend_resid, color = Var)) +geom_line(linewidth =0.6, alpha =0.8) +facet_grid(EPU * Var ~ ., scales ="free", labeller =label_wrap_gen(width=8)) +guides(color =guide_legend(nrow =2)) +labs(title ="Shelf Scale Ocean/Climate Metrics - Detrended")
Bacause the slopewater proportion contains NA values, we cannot evaluate it for breaks unless we impute missing values somehow or take a subset of time that is uninterrupted.
Code
# Stirnimann used these values in their paper:# l = 5, 10, 15, 17.5 years, with monthly data# Huber = 1# Subsampling = (l + 1) / 3# Load the function(s)source(here::here("rstars-master","rSTARS.R"))
Code
# Run the regime shift testshelf_indices_rstars <- shelf_indices_detrended %>%split(.$Var) %>%map_dfr(function(.x){ cutoff_length <-ifelse(str_detect(.x$Var[[1]], "index"),12*7,7)# This is only here because we have duplicate dates in the GSI .x <-distinct(.x, Time, .keep_all = T)# Get the results from that x_rstars <-rstars(data.timeseries =as.data.frame( .x[,c("Time", "trend_resid")]),l.cutoff = cutoff_length,pValue =0.05,Huber =1,Endfunction = T,preWhitening = T,OLS = F,MPK = T,IP4 = F,SubsampleSize = (cutoff_length +1)/3,returnResults = T) %>%mutate(EPU = .x$EPU[[1]],Value = .x$Value,shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA)) },.id ="Var" )
The results can be seen below:
Code
# Summarise the breakpoint locationsshelf_shift_points <- shelf_indices_rstars %>%filter(RSI !=0) %>% dplyr::select(Time, Var, EPU, shift_direction)# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( shelf_shift_points, str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = Time, xend = Time, y =-Inf, , yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( shelf_shift_points, !str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = Time, xend = Time, y =-Inf, , yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data = shelf_indices_rstars,aes(Time, Value),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(EPU * Var~., labeller =label_wrap_gen(width =8)) +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="Shelf-Scale Ocean/Climate Metrics - STARS Changepoints",subtitle ="Performed on Detrended Timeseries")
Based on these results, there is some evidence for breakpoints in the Gulf Stream Indices, and not in the NAO index.
For the EPU-scale metrics we have a number of indices available from the ecodata package:
The cold-pool index
The Northeast Channel Slopewater Proportion (from NERACOOS Buoy N)
Metrics of primary production and zooplankton community
Temperature and salinity timeseries specific to each area
Temperature and salinity is from either GLORYS or FVCOM, primary productivity is satellite derived (OC-CCI, SeaWiFS, MODIS-Aqua), and the zooplankton community indices are from the Gulf of Maine CPR transect.
Code
# # There are a ton here. We want primary productivity / chlor a, and maybe anomalies# chl_pp <- ecodata::chl_pp %>% # filter(str_detect(Var, "MONTHLY")) %>% # filter(str_detect(Var, "PPD|CHLOR_A")) %>% # separate(col = "Time", into = c("Period", "Time"), sep = "_") %>% # mutate(Time = as.Date(# str_c(# str_sub(Time, 1, 4),# str_sub(Time, 5, 6), # "01",# sep = "-")))# Annual will make life easierannual_chl_pp <- ecodata::annual_chl_pp %>%filter(str_detect(Var, "MEAN")) %>%separate(col ="Time", into =c("Period", "Time"), sep ="_") %>%mutate(Time =as.Date(str_c( Time,"01-01",sep ="-")))# Just take one cold pool index for nowcold_pool <- ecodata::cold_pool %>%#distinct(Source)filter(Var =="cold_pool_index") %>%mutate(Var =str_c(Source, Var, sep ="_"),Time =as.Date(str_c( Time,"01-01",sep ="-")))# Slopewaterslopewater <- ecodata::slopewater %>%mutate(Time =as.Date(str_c(Time, "-01-01"))) %>%filter(Time >as.Date("1990-01-01")) %>%drop_na()# Combine thoseepu_indices <-bind_rows(annual_chl_pp, slopewater, cold_pool) %>%group_by(Var, EPU) %>%arrange(Time) # Plot themggplot(epu_indices, aes(Time, Value)) +geom_line(linewidth =0.6, alpha =0.8) +facet_grid(Var * EPU ~ ., scales ="free", labeller =label_wrap_gen(width=8)) +guides(color =guide_legend(nrow =3)) +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +guides(color =guide_legend(nrow =2)) +labs(title ="EPU Scale Ocean/Ecological Metrics - Raw")
Detrending Ecodata Indicators
Most of these indicators have data at the monthly time-scale. To aid in regime change detection long-term year over year changes have been removed.
Once detrended, they can be checked for signs of a regime change.
Code
# Run rstars for those, temperature and salinity are done# Run the regime shift testepu_indices_rstars <- epu_indices_detrended %>%#filter(str_detect(Var, "proportion") == FALSE) %>% split(.$var_epu) %>%map_dfr(function(.x){# This is only here because we have duplicate dates in the GSI .x <-distinct(.x, Time, .keep_all = T)# cutoff length cutoff_length <-7# Get the results from that x_rstars <-rstars(data.timeseries =as.data.frame( .x[,c("Time", "trend_resid")]),l.cutoff = cutoff_length,pValue =0.05,Huber =1,Endfunction = T,preWhitening = T,OLS = F,MPK = T,IP4 = F,SubsampleSize = (cutoff_length +1)/3,returnResults = T) %>%mutate(Value = .x$Value,shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA)) },.id ="var_epu" ) %>%separate(var_epu, into =c("Var", "EPU"), sep ="X")
Primary Production and Cold-Pool Dynamics
The results from the STARS algorithm can be seen below:
Work by Andy Pershing helped develop an understanding that the Gulf of Mane’s zooplankton community in a given year is often one of two groups with different life history and size characteristics. There is a large copepod community, of which Calanus finmarchicus (a large bodied, lipid rich species) is prominent, and a second community which is composed of smaller-bodied and more opportunistic zooplankton species. These two communities compete for the same prey resources, and are typically out of phase with one-another. A principal component analysis using the continuous plankton recorded data has been used as a proxy for which community is dominant each year.
Code
# From Pershing & Kemberling# PC1 explains 53.62% of variance# PC2 explains 27.9%# PC1 is associated with centropages, oithona, para-pseudocalanus# PC2 is C. Fin, Metridia, & Euphausiacea# Load the CPR datacpr_community <-read_csv(here::here("local_data", "cpr_focal_pca_timeseries_period_1961-2017.csv")) %>%rename(PC1_small_zoo =`First Mode`,PC2_large_zoo =`Second Mode`) %>%select(-c(pca_period, taxa_used)) %>%pivot_longer(cols =starts_with("PC"), names_to ="Var", values_to ="value")
Taking PCA timeseries as proxies for those communities and evaluating them for breakpoints gives the following results.
Code
# Run breakpoints in CPR PCA# Run the regime shift testcpr_indices_rstars <- cpr_community %>%filter(year >1976) %>%mutate(EPU ="GOM",var_epu =str_c(Var, "X", EPU)) %>%split(.$var_epu) %>%map_dfr(function(.x){# detrend trend_mod <-lm(value ~ year, data = .x) .x$trend_resid <-resid(trend_mod)# cutoff length cutoff_length <-7# Get the results from that x_rstars <-rstars(data.timeseries =as.data.frame( .x[,c("year", "trend_resid")]),l.cutoff = cutoff_length,pValue =0.05,Huber =1,Endfunction = T,preWhitening = T,OLS = F,MPK = T,IP4 = F,SubsampleSize = (cutoff_length +1)/3,returnResults = T) %>%mutate(Value = .x$value,shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA)) },.id ="var_epu" ) %>%separate(var_epu, into =c("Var", "EPU"), sep ="X") %>%mutate(time =as.Date(str_c(year, "-01-01")))
# Abundance per 100m3 for different taxa# ecodata::zoo_regime %>% distinct(Var) %>% pull() %>% sort()ecomon_zoo <- ecodata::zoo_regimeecomon_zoo %>%filter(!str_detect(Var, "fish|clauso|gas")) %>%ggplot(aes(Time, Value)) +geom_line() +facet_grid(Var ~ EPU)
Code
# We might be able to just pull out the seven focal species and then repeat the PCA process...# Issues:# ecomon doesn't split the calanus into adult/juvenile# Para and Pseaudocalana are split# Euph also has a Euph1
Abundance anomalies are computed from the expected abundance on the day of sample collection. Abundance anomaly time series are constructed for Centropages typicus, Pseudocalanus spp., Calanus finmarchicus, and total zooplankton biovolume. The small-large copepod size index is computed by averaging the individual abundance anomalies of Pseudocalanus spp., Centropages hamatus, Centropages typicus, and Temora longicornis, and subtracting the abundance anomaly of Calanus finmarchicus. This index tracks the overall dominance of the small bodied copepods relative to the largest copepod in the Northeast U.S. region, Calanus finmarchicus.
Code
# This has "LgCopepods" & "SmCopepods", which could produce large/small index# ecodata::zoo_abundance_anom %>% distinct(Var) %>% pull() %>% sort()zoo_lg_small <- ecodata::zoo_abundance_anom %>%filter(Var %in%c("LgCopepods", "SmCopepods")) %>%mutate(Value =as.numeric(Value)) %>%pivot_wider(values_from ="Value", names_from ="Var") %>%mutate(small_large_index = SmCopepods - LgCopepods)ggplot(zoo_lg_small, aes(Time, small_large_index)) +geom_line() +geom_hline(yintercept =0) +facet_grid(EPU~., scales ="free") +labs(y ="Small-Large Copepod Index\n(More Large Copepods <-----> More Small Copepods)")
Code
# # This is too many vars# ecodata::zooplankton_index %>% distinct(Var) %>% pull()# # # Not informative mechanistically# ecodata::zoo_diversity# # # BOLD move on absolute abundances# ecodata::zoo_strat_abun
NEEDS: MCC & Lobster Predator Indices
There are two EPU-Scale indices that we need to develop. This is the MCC index, and a lobster predator abundance index.
The Gulf of Maine Coastal Current plays an important role in transporting lobster larva and their recruitment form year-to-year. The degree of “connected-ness” of the Western and Eastern portions of this current have been used in the past to inform expectations of lobster recruitment.
Local/Nearshore Shifts
Temperature and Salinity Breaks
Code
# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( inshore_tempsal_shifts, str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( inshore_tempsal_shifts, !str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data =filter(inshore_tempsal),aes(time, detrended_vals),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(area_id~Var, labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1978-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="FVCOM Inshore Scale Sal Metrics",subtitle ="STARS Regime Shifts")
Salinity
Code
# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( inshore_tempsal_shifts,str_detect(Var, "Salinity"),str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( inshore_tempsal_shifts, str_detect(Var, "Salinity"),!str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data =filter(inshore_tempsal, str_detect(Var, "Salinity")),aes(time, detrended_vals),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(area_id~Var, labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1978-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Salinity Anomaly",x ="Date",title ="FVCOM Hindcast Inshore Salinity",subtitle ="STARS Regime Shifts")
In addition to breaks in absolute temperatures, there is interest in the amount of time spent in favorable (12-18C) and unfavorable conditions (20C).
These use daily bottom temperatures:
Code
# Load the data for the new regionsdaily_fvcom_temps <-read_csv(str_c(lob_ecol_path, "FVCOM_processed/area_timeseries/new_regions_fvcom_temperatures_daily.csv")) %>%mutate(depth_type =if_else(area_id %in%c("gom_gbk", "SNE"), "offshore", "nearshore"),area_id =factor(area_id, levels = areas_northsouth) )# # Monthly Salinity# monthly_fvcom_sal <- read_csv(# str_c(lob_ecol_path, "FVCOM_processed/area_timeseries/new_regions_fvcom_salinity_monthly_gom3.csv")) %>% # mutate(# depth_type = if_else(area_id %in% c("gom_gbk", "SNE"), "offshore", "nearshore"),# area_id = factor(area_id, levels = areas_northsouth)# )# The degday functions were built with the ability to model daily cycles# the sine and triangle methods accomodate this# since we only have daily data the simple average is probably the way to do itthresh_low <-10thresh_up <-18# library(degday)# Get Monthly Totals in Rangesdd_monthly <- daily_fvcom_temps %>%mutate(year = lubridate::year(time),month = lubridate::month(time),opt_btemp =if_else(between(bottom_t, 10,18), 1, 0),stress_btemp =if_else(bottom_t >18, 1, NA),cold_btemp =if_else(bottom_t <10, 1, NA)) %>%group_by(area_id, year, month) %>%summarise(across(ends_with("temp"), ~sum(.x, na.rm = T)),.groups ="drop") %>%pivot_longer(cols =ends_with("temp"), names_to ="var", values_to ="totals") %>%mutate(time =as.Date(str_c(year,"-01-01")) +months(month-1),area_id =factor(area_id, levels = areas_northsouth),var =case_match( var,"opt_btemp"~"Preferred Bottom Temperatures 10-18C", "stress_btemp"~"Heat Stress Conditions >18C","cold_btemp"~"Below Preferred Conditions <10C") )
Code
# Do annual, Monthly values looked insanedd_annual <- dd_monthly %>%group_by(year, area_id, var) %>%summarise(across(totals, sum)) %>%mutate(var_area =str_c(var, area_id, sep ="X"),time =as.Date(str_c(year, "-01-01")))# Plot themdd_annual %>%ggplot() +geom_area(aes(year, y = totals, fill = var)) +scale_fill_manual(values =c("lightblue", "#ea4f12", "#057872")) +facet_grid(area_id~var) +scale_x_continuous(expand =expansion(add =c(0,0))) +theme(strip.text.y =element_text(angle =0),legend.position ="bottom") +guides(fill =guide_legend(nrow =2,title.position ="top",title.hjust =0.5))+labs(y ="Days in Range",fill ="Daily Temperature Conditions", color ="",title ="FVCOM Bottom Temperature Degree-Days")
---title: "Regime Shift Review"description: | Detailing the Regime Shifts in Maine Coastal Current Behaviordate: "Updated on: `r Sys.Date()`"format: html: code-fold: true code-tools: true df-print: kable self-contained: trueexecute: echo: true warning: false message: false fig.align: center comment: ""---```{r}{library(sf) library(fvcom) library(tidyverse)library(gmRi)library(patchwork)library(rnaturalearth)library(showtext)library(ncdf4)# Cyclic color palettes in scico# From: https://www.fabiocrameri.ch/colourmaps/library(scico)library(legendry)library(ggh4x)}# namespace conflictsconflicted::conflict_prefer("select", "dplyr")conflicted::conflict_prefer("filter", "dplyr")# Set the themetheme_set(theme_gmri_simple() +theme(strip.text.y =element_text(angle =0),legend.position ="bottom", legend.title.position ="top", legend.title =element_text(hjust =0.5), strip.text =element_text(size =8),axis.text =element_text(size =8)))# Project pathslob_ecol_path <-cs_path("mills", "Projects/Lobster ECOL")fvcom_path <-cs_path("res", "FVCOM/Lobster-ECOL")poly_paths <-cs_path("mills", "Projects/Lobster ECOL/Spatial_Defs")# Shapefilesnew_england <-ne_states("united states of america", returnclass ="sf") %>%filter(postal %in%c("VT", "ME", "RI", "MA", "CT", "NH", "NY", "MD", "VA", "NJ", "DE", "NC", "PA", "WV"))canada <-ne_states("canada", returnclass ="sf")# # # Support functions for FVCOMsource(here::here("R/FVCOM_Support.R"))# New areas factor levelsareas_northsouth <-c("eastern maine", "central maine", "western maine", "eastern mass","southern mass", "rhode island shore","long island sound", "new jersey shore", "five fathom bank", "virginia shore","gom_gbk", "sne")``````{r}#| label: style-sheet#| results: asis# Use GMRI styleuse_gmri_style_rmd()``````{r}#| label: load shapes# Read Regions inproj_path <-cs_path("mills", "Projects/Lobster ECOL")# Load Shapefiles for inshore/offshorepoly_paths <-cs_path("mills", "Projects/Lobster ECOL/Spatial_Defs")# Load inshore areas# 12nm from shore, aggregates of statistical zonesinshore_areas <-map_dfr(setNames(list.files(str_c(poly_paths, "inshore_areas"), full.names = T),str_remove(list.files(str_c(poly_paths, "inshore_areas")), ".geojson")),function(x){read_sf(x)}) offshore_areas <-map_dfr(setNames(list.files(str_c(poly_paths, "offshore_areas"), full.names = T),str_remove(list.files(str_c(poly_paths, "offshore_areas")), ".geojson")),function(x){read_sf(x)}) %>%mutate(SHORT_NAME = Region) # st_crs(offshore_areas)# st_crs(inshore_areas)# Combine so we can plot themstudy_regions <-bind_rows(st_transform(inshore_areas, crs =st_crs(4269)), offshore_areas)# NEW Areas``````{r}# clusters of statistical areas that align loosely with geography and management areasinshore_areas <-read_sf(str_c(poly_paths,"spatial_defs_2025/12nm_poly_statarea_merge.shp")) %>% janitor::clean_names() %>%mutate(area_type ="nearshore-coastal",area_id =tolower(short_name))# ecological production unitsoffshore_areas <-read_sf(str_c(poly_paths,"spatial_defs_2025/sne_gom_tocoast.shp")) %>% janitor::clean_names() %>%mutate(area_type ="offshore-regional",area_id =tolower(region))# Combine themstudy_regions <-bind_rows(st_transform(dplyr::select(inshore_areas, area_id, geometry), st_crs(offshore_areas)), dplyr::select(offshore_areas, area_id, geometry))``````{r}#| label: fonts-config#| echo: false# Path to the directory containing the font file (replace with your actual path)font_dir <-paste0(system.file("stylesheets", package ="gmRi"), "/GMRI_fonts/Avenir/")# Register the fontfont_add(family ="Avenir",file.path(font_dir, "LTe50342.ttf"),bold =file.path(font_dir, "LTe50340.ttf"),italic =file.path(font_dir, "LTe50343.ttf"),bolditalic =file.path(font_dir, "LTe50347.ttf"))# Load the fontshowtext::showtext_auto()```# Reviewing STARS Regime Changes in Space/TimeThis markdown reviews the various rstars regime shift results which were produced separately. I will begin at the largest geographic scales and work down to local timeseries:Regime shifts for individual timeseries were tested using the STARS methodology. Any daily timeseries (temperature and salinity from ocean reanalysis models) were aggregated to a monthly temporal resolution, and any trends and seasonal cycles were removed.The Marriott, Pope and Kendall (MPK) "pre-whitening" routine was used within the {rstars} algorithm to remove "red noise" (autoregressive processes, typically AR1) from the timeseries.For more details on trend removal and pre-whitening methods see Rodionov 2006.## Shelf-Scale ShiftsThere are 2-3 climate and oceanographic timeseries that operate at the broad regional scale of the Northeast shelf. These are:1. The Gulf Stream Index (a metric indicating the North/South position of the Gulf Stream) based on SSH2. The Northeast Channel Slopewater Proportions (the percentage of various water masses at the 150-200m depth entering GOM, using NERACOOS buoy data)3. The North Atlantic Oscillation (atmospheric pressure differential between icelandic low and the Azores High)These three indices affect conditions over large spatial scales and and are likely to either directly or indirectly impact more local scale changes.These three metrics are available in the `ecodata` r package and can be pulled directly from the package.```{r}library(ecodata)# GSI# Why is there more than one value per month?gsi <- ecodata::gsi %>%#glimpse()mutate(Time =as.Date(str_c(str_replace(Time, "[.]", "-"), "-01")))# use the old onegsi_old <- ecodata::gsi_old %>%#glimpse()mutate(Var ="gulf stream index old") %>%mutate(Time =as.Date(str_c(str_replace(Time, "[.]", "-"), "-01")))# NAOnao <- ecodata::nao%>%mutate(Time =as.Date(str_c(Time, "-01-01")))# Put them together to plotshelf_indices <-bind_rows(list(gsi, gsi_old, nao))```The Gulf Stream indices come as two monthly datasets, the other indices are annual. Any long-term trends for each metric have been removed prior to regime shift tests on these metrics.```{r}#| fig-height: 5# Run the shift test for summertime PCAshelf_indices_detrended <- shelf_indices %>%split(.$Var) %>%map_dfr(function(.x){# Detrend .x <- .x %>%arrange(Time) %>%mutate(time = Time,yr_num =year(time))# annual trend trend_mod <-lm(Value ~ Time, data = .x)# save the results .x <- broom::augment(x = trend_mod) %>%rename(trend_fit = .fitted,trend_resid = .resid) %>%full_join(.x, join_by(Time, Value)) %>%mutate(trend_resid =if_else(is.na(Value), NA, trend_resid))return(.x)})# Plot the residuals from the trendggplot(shelf_indices_detrended, aes(Time, trend_resid, color = Var)) +geom_line(linewidth =0.6, alpha =0.8) +facet_grid(EPU * Var ~ ., scales ="free", labeller =label_wrap_gen(width=8)) +guides(color =guide_legend(nrow =2)) +labs(title ="Shelf Scale Ocean/Climate Metrics - Detrended")```Bacause the slopewater proportion contains NA values, we cannot evaluate it for breaks unless we impute missing values somehow or take a subset of time that is uninterrupted.```{r}# Stirnimann used these values in their paper:# l = 5, 10, 15, 17.5 years, with monthly data# Huber = 1# Subsampling = (l + 1) / 3# Load the function(s)source(here::here("rstars-master","rSTARS.R"))``````{r}# Run the regime shift testshelf_indices_rstars <- shelf_indices_detrended %>%split(.$Var) %>%map_dfr(function(.x){ cutoff_length <-ifelse(str_detect(.x$Var[[1]], "index"),12*7,7)# This is only here because we have duplicate dates in the GSI .x <-distinct(.x, Time, .keep_all = T)# Get the results from that x_rstars <-rstars(data.timeseries =as.data.frame( .x[,c("Time", "trend_resid")]),l.cutoff = cutoff_length,pValue =0.05,Huber =1,Endfunction = T,preWhitening = T,OLS = F,MPK = T,IP4 = F,SubsampleSize = (cutoff_length +1)/3,returnResults = T) %>%mutate(EPU = .x$EPU[[1]],Value = .x$Value,shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA)) },.id ="Var" )```The results can be seen below:```{r}# Summarise the breakpoint locationsshelf_shift_points <- shelf_indices_rstars %>%filter(RSI !=0) %>% dplyr::select(Time, Var, EPU, shift_direction)# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( shelf_shift_points, str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = Time, xend = Time, y =-Inf, , yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( shelf_shift_points, !str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = Time, xend = Time, y =-Inf, , yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data = shelf_indices_rstars,aes(Time, Value),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(EPU * Var~., labeller =label_wrap_gen(width =8)) +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="Shelf-Scale Ocean/Climate Metrics - STARS Changepoints",subtitle ="Performed on Detrended Timeseries")```Based on these results, there is some evidence for breakpoints in the Gulf Stream Indices, and not in the NAO index.```{r}shelf_shift_points %>%group_by(Var) %>%arrange(Time, EPU) %>% gt::gt() %>% gt::tab_header(title ="Shelf-Scale Breaks")```## EPU-Scale Ecodata IndicesFor the EPU-scale metrics we have a number of indices available from the `ecodata` package:1. The cold-pool index2. The Northeast Channel Slopewater Proportion (from NERACOOS Buoy N)3. Metrics of primary production and zooplankton community4. Temperature and salinity timeseries specific to each areaTemperature and salinity is from either GLORYS or FVCOM, primary productivity is satellite derived (OC-CCI, SeaWiFS, MODIS-Aqua), and the zooplankton community indices are from the Gulf of Maine CPR transect.```{r}# # There are a ton here. We want primary productivity / chlor a, and maybe anomalies# chl_pp <- ecodata::chl_pp %>% # filter(str_detect(Var, "MONTHLY")) %>% # filter(str_detect(Var, "PPD|CHLOR_A")) %>% # separate(col = "Time", into = c("Period", "Time"), sep = "_") %>% # mutate(Time = as.Date(# str_c(# str_sub(Time, 1, 4),# str_sub(Time, 5, 6), # "01",# sep = "-")))# Annual will make life easierannual_chl_pp <- ecodata::annual_chl_pp %>%filter(str_detect(Var, "MEAN")) %>%separate(col ="Time", into =c("Period", "Time"), sep ="_") %>%mutate(Time =as.Date(str_c( Time,"01-01",sep ="-")))# Just take one cold pool index for nowcold_pool <- ecodata::cold_pool %>%#distinct(Source)filter(Var =="cold_pool_index") %>%mutate(Var =str_c(Source, Var, sep ="_"),Time =as.Date(str_c( Time,"01-01",sep ="-")))# Slopewaterslopewater <- ecodata::slopewater %>%mutate(Time =as.Date(str_c(Time, "-01-01"))) %>%filter(Time >as.Date("1990-01-01")) %>%drop_na()# Combine thoseepu_indices <-bind_rows(annual_chl_pp, slopewater, cold_pool) %>%group_by(Var, EPU) %>%arrange(Time) # Plot themggplot(epu_indices, aes(Time, Value)) +geom_line(linewidth =0.6, alpha =0.8) +facet_grid(Var * EPU ~ ., scales ="free", labeller =label_wrap_gen(width=8)) +guides(color =guide_legend(nrow =3)) +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +guides(color =guide_legend(nrow =2)) +labs(title ="EPU Scale Ocean/Ecological Metrics - Raw")```### Detrending Ecodata IndicatorsMost of these indicators have data at the monthly time-scale. To aid in regime change detection long-term year over year changes have been removed.```{r}#| fig.height: 6# Detrend the epu stuffepu_indices_detrended <- epu_indices %>%mutate(var_epu =str_c(Var, EPU, sep ="X")) %>%split(.$var_epu) %>%map_dfr(function(.x){# Detrend .x <- .x %>%arrange(Time) %>%mutate(time = Time,yr_num =year(time))# annual trend trend_mod <-lm(Value ~ Time, data = .x)# save the results .x <- broom::augment(x = trend_mod) %>%rename(trend_fit = .fitted,trend_resid = .resid) %>%full_join(.x, join_by(Time, Value)) %>%mutate(trend_resid =if_else(is.na(Value), NA, trend_resid))return(.x)})# Plot detrendedggplot(epu_indices_detrended, aes(Time, trend_resid)) +geom_line(linewidth =0.6, alpha =0.8) +facet_grid(EPU * Var ~ ., scales ="free", labeller =label_wrap_gen(width=8)) +guides(color =guide_legend(nrow =3)) +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +labs(title ="EPU Scale Ocean/Climate Metrics - Detrended",y ="Metric")```Once detrended, they can be checked for signs of a regime change.```{r}# Run rstars for those, temperature and salinity are done# Run the regime shift testepu_indices_rstars <- epu_indices_detrended %>%#filter(str_detect(Var, "proportion") == FALSE) %>% split(.$var_epu) %>%map_dfr(function(.x){# This is only here because we have duplicate dates in the GSI .x <-distinct(.x, Time, .keep_all = T)# cutoff length cutoff_length <-7# Get the results from that x_rstars <-rstars(data.timeseries =as.data.frame( .x[,c("Time", "trend_resid")]),l.cutoff = cutoff_length,pValue =0.05,Huber =1,Endfunction = T,preWhitening = T,OLS = F,MPK = T,IP4 = F,SubsampleSize = (cutoff_length +1)/3,returnResults = T) %>%mutate(Value = .x$Value,shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA)) },.id ="var_epu" ) %>%separate(var_epu, into =c("Var", "EPU"), sep ="X")```### Primary Production and Cold-Pool DynamicsThe results from the STARS algorithm can be seen below:```{r}# Summarise the breakpoint locationsepu_shift_points <- epu_indices_rstars %>%filter(RSI !=0) %>% dplyr::select(Time, Var, EPU, shift_direction)# Plot the breaks over the monthly dataggplot() +geom_vline(data = epu_shift_points,aes(xintercept = Time,color = shift_direction),linewidth =1.5) +geom_line(data = epu_indices_rstars,aes(Time, Value),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(EPU * Var~., labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="EPU Scale Ocean/Climate Metrics - Detrended",subtitle ="STARS Regime Shifts")```Based on these results, we see no breakpoints in primary production or the cold pool indices.```{r}epu_shift_points %>%group_by(Var) %>%arrange(Time, EPU) %>% gt::gt() %>% gt::tab_header(title ="Ecodata EPU Scale Breakpoints")```### EPU/Offshore Temperature and SalinityTemperature and salinity changes were done separately, here are their results```{r}# Load Temperature and Salinity, Pull out the EPU scale timeseries# Load the regime shift results from STARS_FVCOM.qmdsurf_sal_monthly_shifts <-read_csv( here::here("rstars_results/lobecol_ssal_monthly_shifts_detrended.csv")) %>%mutate(Var ="Surface Salinity") %>%rename(detrended_vals = ssal_model_resid) bot_sal_monthly_shifts <-read_csv( here::here("rstars_results/lobecol_bsal_monthly_shifts_detrended.csv")) %>%mutate(Var ="Bottom Salinity") %>%rename(detrended_vals = bsal_model_resid)surf_temp_monthly_shifts <-read_csv( here::here("rstars_results/lobecol_stemp_monthly_shifts_detrended.csv")) %>%mutate(Var ="Surface Temperature") %>%rename(detrended_vals = stemp_model_resid)bot_temp_monthly_shifts <-read_csv( here::here("rstars_results/lobecol_btemp_monthly_shifts_detrended.csv")) %>%mutate(Var ="Bottom Temperature") %>%rename(detrended_vals = btemp_model_resid)# Put them togethertempsal <-bind_rows(list(surf_sal_monthly_shifts, bot_sal_monthly_shifts, surf_temp_monthly_shifts, bot_temp_monthly_shifts)) %>%mutate(shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA),area_id =factor(area_id, levels = areas_northsouth))offshore_tempsal <- tempsal %>%filter(area_id %in%tolower(c("GOM_GBK", "SNE")))inshore_tempsal <- tempsal %>%filter(area_id %in%tolower(c("GOM_GBK", "SNE")) ==FALSE)# Pull the shift pointstempsal_shifts <- tempsal %>%filter(RSI !=0)offshore_tempsal_shifts <- offshore_tempsal %>%filter(RSI !=0) %>% dplyr::select(time, Var, area_id, shift_direction)inshore_tempsal_shifts <- inshore_tempsal %>%filter(RSI !=0) %>% dplyr::select(time, Var, area_id, shift_direction)``````{r}#| fig.height: 5# Plot the offshore shifts# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( offshore_tempsal_shifts, str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( offshore_tempsal_shifts, !str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data = offshore_tempsal,aes(time, detrended_vals),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_nested(area_id * Var~., labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1978-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="FVCOM EPU Scale Temp/Sal Metrics",subtitle ="STARS Regime Shifts")```A change in SNE salinity appears to have occured around 1992.Surface temperatures fell in SNE around 2002, but they rose again in 2011 along with GOM+GBK the same year.```{r}offshore_tempsal_shifts %>%group_by(Var) %>%arrange(time, area_id) %>% gt::gt() %>% gt::tab_header(title ="EPU Scale Temp+Sal Breaks")```### CPR Community PCA IndexWork by Andy Pershing helped develop an understanding that the Gulf of Mane's zooplankton community in a given year is often one of two groups with different life history and size characteristics. There is a large copepod community, of which Calanus finmarchicus (a large bodied, lipid rich species) is prominent, and a second community which is composed of smaller-bodied and more opportunistic zooplankton species. These two communities compete for the same prey resources, and are typically out of phase with one-another. A principal component analysis using the continuous plankton recorded data has been used as a proxy for which community is dominant each year.```{r}# From Pershing & Kemberling# PC1 explains 53.62% of variance# PC2 explains 27.9%# PC1 is associated with centropages, oithona, para-pseudocalanus# PC2 is C. Fin, Metridia, & Euphausiacea# Load the CPR datacpr_community <-read_csv(here::here("local_data", "cpr_focal_pca_timeseries_period_1961-2017.csv")) %>%rename(PC1_small_zoo =`First Mode`,PC2_large_zoo =`Second Mode`) %>%select(-c(pca_period, taxa_used)) %>%pivot_longer(cols =starts_with("PC"), names_to ="Var", values_to ="value")```Taking PCA timeseries as proxies for those communities and evaluating them for breakpoints gives the following results.```{r}# Run breakpoints in CPR PCA# Run the regime shift testcpr_indices_rstars <- cpr_community %>%filter(year >1976) %>%mutate(EPU ="GOM",var_epu =str_c(Var, "X", EPU)) %>%split(.$var_epu) %>%map_dfr(function(.x){# detrend trend_mod <-lm(value ~ year, data = .x) .x$trend_resid <-resid(trend_mod)# cutoff length cutoff_length <-7# Get the results from that x_rstars <-rstars(data.timeseries =as.data.frame( .x[,c("year", "trend_resid")]),l.cutoff = cutoff_length,pValue =0.05,Huber =1,Endfunction = T,preWhitening = T,OLS = F,MPK = T,IP4 = F,SubsampleSize = (cutoff_length +1)/3,returnResults = T) %>%mutate(Value = .x$value,shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA)) },.id ="var_epu" ) %>%separate(var_epu, into =c("Var", "EPU"), sep ="X") %>%mutate(time =as.Date(str_c(year, "-01-01")))``````{r}# Summarise the breakpoint locationscpr_shift_points <- cpr_indices_rstars %>%filter(RSI !=0) %>% dplyr::select(time, Var, EPU, shift_direction)# Plot the breaks over the monthly dataggplot() +geom_vline(data = cpr_shift_points,aes(xintercept = time,color = shift_direction),linewidth =1.5) +geom_line(data = cpr_indices_rstars,aes(time, trend_resid),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(Var ~ EPU, labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="CPR Zooplankton Community Metrics",subtitle ="STARS Regime Shifts")```### ECOMON Community PCA Index```{r}# Abundance per 100m3 for different taxa# ecodata::zoo_regime %>% distinct(Var) %>% pull() %>% sort()ecomon_zoo <- ecodata::zoo_regimeecomon_zoo %>%filter(!str_detect(Var, "fish|clauso|gas")) %>%ggplot(aes(Time, Value)) +geom_line() +facet_grid(Var ~ EPU)# We might be able to just pull out the seven focal species and then repeat the PCA process...# Issues:# ecomon doesn't split the calanus into adult/juvenile# Para and Pseaudocalana are split# Euph also has a Euph1```### Large and Small Copepod Indexhttps://noaa-edab.github.io/tech-doc/zoo_abundance_anom.html?q=zoopl#copepod> Abundance anomalies are computed from the expected abundance on the day of sample collection. Abundance anomaly time series are constructed for Centropages typicus, Pseudocalanus spp., Calanus finmarchicus, and total zooplankton biovolume. The small-large copepod size index is computed by averaging the individual abundance anomalies of Pseudocalanus spp., Centropages hamatus, Centropages typicus, and Temora longicornis, and subtracting the abundance anomaly of Calanus finmarchicus. This index tracks the overall dominance of the small bodied copepods relative to the largest copepod in the Northeast U.S. region, Calanus finmarchicus.```{r}# This has "LgCopepods" & "SmCopepods", which could produce large/small index# ecodata::zoo_abundance_anom %>% distinct(Var) %>% pull() %>% sort()zoo_lg_small <- ecodata::zoo_abundance_anom %>%filter(Var %in%c("LgCopepods", "SmCopepods")) %>%mutate(Value =as.numeric(Value)) %>%pivot_wider(values_from ="Value", names_from ="Var") %>%mutate(small_large_index = SmCopepods - LgCopepods)ggplot(zoo_lg_small, aes(Time, small_large_index)) +geom_line() +geom_hline(yintercept =0) +facet_grid(EPU~., scales ="free") +labs(y ="Small-Large Copepod Index\n(More Large Copepods <-----> More Small Copepods)")``````{r}#| label: zooplankton, not used# # This is too many vars# ecodata::zooplankton_index %>% distinct(Var) %>% pull()# # # Not informative mechanistically# ecodata::zoo_diversity# # # BOLD move on absolute abundances# ecodata::zoo_strat_abun```### NEEDS: MCC & Lobster Predator IndicesThere are two EPU-Scale indices that we need to develop. This is the MCC index, and a lobster predator abundance index.The Gulf of Maine Coastal Current plays an important role in transporting lobster larva and their recruitment form year-to-year. The degree of "connected-ness" of the Western and Eastern portions of this current have been used in the past to inform expectations of lobster recruitment.## Local/Nearshore Shifts### Temperature and Salinity Breaks```{r}#| fig.height: 8# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( inshore_tempsal_shifts, str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( inshore_tempsal_shifts, !str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data =filter(inshore_tempsal),aes(time, detrended_vals),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(area_id~Var, labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1978-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="FVCOM Inshore Scale Sal Metrics",subtitle ="STARS Regime Shifts")```#### Salinity ```{r}#| fig.height: 8# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( inshore_tempsal_shifts,str_detect(Var, "Salinity"),str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( inshore_tempsal_shifts, str_detect(Var, "Salinity"),!str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data =filter(inshore_tempsal, str_detect(Var, "Salinity")),aes(time, detrended_vals),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(area_id~Var, labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1978-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Salinity Anomaly",x ="Date",title ="FVCOM Hindcast Inshore Salinity",subtitle ="STARS Regime Shifts")``````{r}inshore_tempsal_shifts %>%filter(str_detect(Var, "Salinity")) %>%group_by(Var) %>%arrange(time, area_id) %>% gt::gt() %>% gt::tab_header(title ="Inshore Salinity Regime Breaks")``````{r}# Highlight whichever regions have seen salinity regime changeggplot() +geom_sf(data =filter( study_regions, area_id %in% (dplyr::filter(tempsal_shifts, str_detect(Var, "Salinity")) %>%pull(area_id)) ),fill =gmri_cols("gmri blue"), alpha =0.4) +geom_sf(data = new_england) +geom_sf(data = canada) +coord_sf(xlim =c(-78, -66), ylim =c(35.5, 45)) +labs(title ="Salinity Regime Change Affected Areas")```#### Temperatures```{r}#| fig.height: 8# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( inshore_tempsal_shifts,!str_detect(Var, "Salinity"),str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( inshore_tempsal_shifts, !str_detect(Var, "Salinity"),!str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data =filter(inshore_tempsal, !str_detect(Var, "Salinity")),aes(time, detrended_vals),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(area_id~Var, labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1978-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Temperature Anomaly",x ="Date",title ="FVCOM Hindcast Inshore Temperature",subtitle ="STARS Regime Shifts")``````{r}ggplot() +geom_sf(data =filter( study_regions, area_id %in% (dplyr::filter(inshore_tempsal_shifts, !str_detect(Var, "Salinity")) %>%pull(area_id)) ),fill =gmri_cols("lv orange"), alpha =0.4) +geom_sf(data = new_england) +geom_sf(data = canada) +coord_sf(xlim =c(-78, -66), ylim =c(35.5, 45)) +labs(title ="Inshore Temperature Regime Shift Affected Areas")``````{r}inshore_tempsal_shifts %>%filter(!str_detect(Var, "Salinity")) %>%group_by(Var) %>%arrange(time, area_id) %>% gt::gt() %>% gt::tab_header(title ="Inshore Scale Temperature Regime Breaks")```### Days in Key Temperature RangesIn addition to breaks in absolute temperatures, there is interest in the amount of time spent in favorable (12-18C) and unfavorable conditions (20C).These use daily bottom temperatures:```{r}# Load the data for the new regionsdaily_fvcom_temps <-read_csv(str_c(lob_ecol_path, "FVCOM_processed/area_timeseries/new_regions_fvcom_temperatures_daily.csv")) %>%mutate(depth_type =if_else(area_id %in%c("gom_gbk", "SNE"), "offshore", "nearshore"),area_id =factor(area_id, levels = areas_northsouth) )# # Monthly Salinity# monthly_fvcom_sal <- read_csv(# str_c(lob_ecol_path, "FVCOM_processed/area_timeseries/new_regions_fvcom_salinity_monthly_gom3.csv")) %>% # mutate(# depth_type = if_else(area_id %in% c("gom_gbk", "SNE"), "offshore", "nearshore"),# area_id = factor(area_id, levels = areas_northsouth)# )# The degday functions were built with the ability to model daily cycles# the sine and triangle methods accomodate this# since we only have daily data the simple average is probably the way to do itthresh_low <-10thresh_up <-18# library(degday)# Get Monthly Totals in Rangesdd_monthly <- daily_fvcom_temps %>%mutate(year = lubridate::year(time),month = lubridate::month(time),opt_btemp =if_else(between(bottom_t, 10,18), 1, 0),stress_btemp =if_else(bottom_t >18, 1, NA),cold_btemp =if_else(bottom_t <10, 1, NA)) %>%group_by(area_id, year, month) %>%summarise(across(ends_with("temp"), ~sum(.x, na.rm = T)),.groups ="drop") %>%pivot_longer(cols =ends_with("temp"), names_to ="var", values_to ="totals") %>%mutate(time =as.Date(str_c(year,"-01-01")) +months(month-1),area_id =factor(area_id, levels = areas_northsouth),var =case_match( var,"opt_btemp"~"Preferred Bottom Temperatures 10-18C", "stress_btemp"~"Heat Stress Conditions >18C","cold_btemp"~"Below Preferred Conditions <10C") )``````{r}# Do annual, Monthly values looked insanedd_annual <- dd_monthly %>%group_by(year, area_id, var) %>%summarise(across(totals, sum)) %>%mutate(var_area =str_c(var, area_id, sep ="X"),time =as.Date(str_c(year, "-01-01")))# Plot themdd_annual %>%ggplot() +geom_area(aes(year, y = totals, fill = var)) +scale_fill_manual(values =c("lightblue", "#ea4f12", "#057872")) +facet_grid(area_id~var) +scale_x_continuous(expand =expansion(add =c(0,0))) +theme(strip.text.y =element_text(angle =0),legend.position ="bottom") +guides(fill =guide_legend(nrow =2,title.position ="top",title.hjust =0.5))+labs(y ="Days in Range",fill ="Daily Temperature Conditions", color ="",title ="FVCOM Bottom Temperature Degree-Days")``````{r}# # Remove monthly averages, and trendsdd_annual_detrended <- dd_annual %>%split(.$var_area) %>%map_dfr(function(.x){# Detrend .x <- .x %>%arrange(year) %>%mutate(yr_num =as.numeric(year))# annual trend + monthly average trend_mod <-lm(totals ~ yr_num, data = .x)# save the results .x <- broom::augment(x = trend_mod) %>%rename(trend_fit = .fitted,trend_resid = .resid) %>%full_join(.x, join_by(yr_num, totals)) %>%mutate(trend_resid =if_else(is.na(totals), NA, trend_resid))return(.x)}) %>%mutate(time =as.Date(str_c(year, "-01-01")))# Plotdd_annual_detrended %>%filter(area_id %in%tolower(c("GOM_GBK", "SNE"))) %>%ggplot(aes(time, trend_resid, fill = var, color = var)) +geom_col() +geom_smooth(method ="loess", linewidth =0.6) +scale_color_manual(values =c("lightblue", "#ea4f12", "#057872")) +scale_fill_manual(values =c("lightblue", "#ea4f12", "#057872")) +facet_grid(area_id~var, scales ="free", labeller =label_wrap_gen(width=8)) +guides(color =guide_legend(nrow =3)) +labs(title ="EPU Scale Ocean/Climate Metrics - Detrended",y ="Departure from Long-Term Trend (days)")``````{r}# Run the regime shift testtemp_range_rstars <- dd_annual_detrended %>%# temp_range_rstars <- dd_annual %>% split(.$var_area) %>%map_dfr(function(.x){# Seven years cutoff_length <-7# Get the results from that x_rstars <-rstars(data.timeseries =as.data.frame( .x[,c("time", "trend_resid")]),l.cutoff = cutoff_length,pValue =0.05,Huber =1,Endfunction = T,preWhitening = T,OLS = F,MPK = T,IP4 = F,SubsampleSize = (cutoff_length +1)/3,returnResults = T) %>%mutate(Value = .x$totals,shift_direction =case_when( RSI >0~"Shift Up", RSI <0~"Shift Down",TRUE~NA)) },.id ="Var" )%>%separate(Var, into =c("Var", "area_id"), sep ="X") %>%mutate(area_id =factor(area_id, levels = areas_northsouth))```### Temperature Suitability ShiftsThe results can be seen below:```{r}#| fig.height: 8# Summarise the breakpoint locationstemp_suit_shift_points <- temp_range_rstars %>%filter(RSI !=0) %>% dplyr::select(time, Var, area_id, shift_direction)# Plot the breaks over the monthly dataggplot() +# Add a vertical line using geom_segment with an arrowgeom_segment(data =filter( temp_suit_shift_points, str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, , yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="last")) +geom_segment(data =filter( temp_suit_shift_points, !str_detect(shift_direction, "Up")),linewidth =0.8,aes(x = time, xend = time, y =-Inf, , yend =Inf, color = shift_direction), arrow =arrow(length =unit(0.3, "cm"), ends ="first")) +geom_line(data = temp_range_rstars,aes(time, Value),linewidth =0.4, alpha =0.5) +scale_color_gmri() +facet_grid(area_id ~ Var, labeller =label_wrap_gen(width =8), scales ="free") +scale_x_date(breaks =seq.Date(from =as.Date("1950-01-01"),to =as.Date("2020-01-01") ,by ="10 year"),limits =as.Date(c("1970-01-01", "2020-01-01")),labels = scales::label_date_short()) +theme(legend.position ="bottom",strip.text.y =element_text(angle =0)) +labs(color ="",y ="Measurement",x ="Date",title ="Lobster Thermal Preferences - Detrended",subtitle ="STARS Regime Shifts")```Based on the annual totals, we see limited breakpoints in suitable thermal habitat.```{r}temp_suit_shift_points %>%group_by(Var) %>%arrange(time, area_id) %>% gt::gt() %>% gt::tab_header(title ="Temperature Suitability Scale Breaks")```And restricted to these areas```{r}ggplot() +geom_sf(data =filter( study_regions, area_id %in% temp_suit_shift_points$area_id),fill =gmri_cols("lv orange"), alpha =0.4) +geom_sf(data = new_england) +geom_sf(data = canada) +coord_sf(xlim =c(-78, -66), ylim =c(35.5, 45)) +labs(title ="Affected Areas")```## Summary Figures / Tables